We follow the evaluation setup by [1]}, as was done by all of the baseline models. We perform a 10-fold cross-validation and report the mean and the standard deviation. In line with baseline models, we train for 350 epochs, and for each of the datasets we perform a grid search over the batch size \(\in \lbrace 32, 128\rbrace \) , hidden units \(\in \lbrace 32, 64, 128\rbrace \)  and number of steps \(\ell \in \lbrace 8, 16\rbrace \) . We always set the number of agents to the mean number of nodes in the graphs of the corresponding dataset (see Table REF ). As REDDIT-BINARY has some very high-degree nodes with thousands of neighbors (Table REF ) this can cause memory issues when many agents end up on the same high-degree node at the same time and compute their transition probabilities. To avoid this, for REDDIT-BINARY we use \(k = 350\)  agents instead of 430 and consider \(\ell \in \lbrace 4, 8\rbrace \) . For the DD dataset, we use the usual setup, as it does not have such high-degree nodes. Neither of the baselines were trained on DD or REDDIT-BINARY datasets (except ESAN on REDDIT-BINARY), thus we trained them using the original code and hyperparameter tuning.
